Standard Use Cases and Algorithms

This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that's not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.
By the end of this course, you will have learned how to:
• Formulate machine learning solutions to real-world problems
• Identify whether the data you have is sufficient for ML
• Carry a project through various ML phases including training, evaluation, and deployment
• Perform AI responsibly and avoid reinforcing existing bias
• Discover ML use cases
• Be successful at ML
You'll need a desktop web browser to run this course's interactive labs via Qwiklabs and Google Cloud Platform.
>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

LL

It's a very good starting point for business professionals interested on a deep overview of Machine Learning, its characteristics, usage and benefits.

RP

Jul 04, 2019

Filled StarFilled StarFilled StarFilled StarFilled Star

Great overview of the basics of AI and how to think about when it makes the most sense to use it, as well as its limitations.

從本節課中

What is Machine Learning?

This module defines what machine learning is, provides examples of how businesses are using it, contextualizes recent advances in machine learning, and reviews how artificial intelligence raises important ethical questions.

教學方

Google Cloud Training

腳本

Let's start with a definition. What is ML. Here's the definition I like to use. ML is a way to get predictive insights from data to make repeated decisions. That may sound complicated, but all the parts of that definition are important. Think of it like the recipe for ML. We will go over all the key ingredients you need to be successful with ML. Think about your business. How do you use data today? Do you have a dashboard that analysts view every day? Or maybe there's a report that your manager's review each month, both the dashboard and the report are examples of backward-looking data. Most analytics in your business is probably backward-looking, where you look at historical data to calculate metrics or identify trends. To create value in your business, you need to use data to make decisions. It is not enough to identify a trend. You need to make decisions based on that trend. Think about this example, a business analyst's reviews a report and sees that the demand is increasing for a specific product in a specific region. The analysts then suggests a new price for that product in that region to increase profit. Now, the business analyst is making a predictive insight, but is that scalable? Can that business analysts make such a decision for every product and every region? Can they dynamically adjust the price every second based on how many people want that item at that very instant? In order to make decisions around predictive insights repeatable, you need machine learning. You need a computer to help you derive those insights. So, machine learning is a tool that enables your business to make many predictive decisions from data. It's about scaling business intelligence and decision-making. Machine learning uses standard algorithms. Normally, when we think of computers, we think of programs that do different things. For example, the software that you use to file your taxes is very different from the software that you use to get directions home. Machine learning is different. You use the same software. That's what we mean when we say ML uses standard algorithms. But you can train that software to do very different things. You can train that software to estimate the tax you owe or you can train the software to estimate the time it will take you to get home. The ML software once trained is termed a model. So, you now have a model that can estimate taxes. Or a model that can estimate the time to get home. The way we do this is to train the ML model with examples. We will train the model to estimate tax by showing it many examples of tax returns. We will train the model to estimate trip duration by showing it many journeys. The first stage of ML is to train an ML model with examples. An example consists of an input and the correct answer for that input. This is called a label. Imagine you work for a manufacturing company. You want to train a machine learning model to detect defects in the parts before they are assembled into products. You would collect a data set of images of parts. Some of these parts will be good. Some of these parts will be fractured. For each image, you will assign the corresponding label and use this set of examples to train the model. After you train the model, you can use it to predict the label of images that it has never seen before. Here your input for the trained model is an image of a part. Because the model has been trained, it is correctly able to predict that this part is in good condition. Note that the image on this slide is different from the ones used for training. It still works because the ML model has generalized from the specific examples you showed it during training to more general idea of what good condition looks like. The key to making an ML model generalize is data and lots of it. Having labeled data is a prerequisite for successful ML. Let's go back to our definition of ML. Remember, we said it's a way to use standard algorithms. But what do we mean when we say standard algorithm? For many ML use cases, which I'll call the standard use cases, we can map directly from the problem to the algorithm used to solve it. Why do we say these algorithms are standard? These algorithms exist independently of the use case. Even though detecting manufacturing defects in images and detecting disease leaves in images are two very different use cases, the same algorithm in image classification network works for both. Similarly, there are standard algorithms for predicting the future value of a time series or to transcribe human speech. Here you can see the standard algorithm used for image classification. It's not crucial to understand how an image classification algorithm works. Only that its the algorithm that you should use if you need to classify images of automotive parts. When we use the same algorithm on different datasets, there are different features or inputs relevant to the different use cases, and you can see them represented visually here. You might be asking yourself isn't the logic different? You can't possibly use the same rules identifying defects in manufacturing that you do in identifying leaves. The logic is different, but ML doesn't use logical rules. The image classification network isn't a set of rules like; if, this, then, that, but a function that learns how to distinguish between categories of images. So, even though we start with the same standard algorithm after training, the trained model that classifies leaves is different from the trained model that classifies parts, and guess what? You can actually reuse the same code for other use cases focused on the same kind of task. So, in our example, we were identifying manufacturing defects, but the higher-level task was classifying images. You can reuse the same code for another image classification problem like finding examples of your products in photos posted on social media. However, you still have to train it separately for each use case.